Методы классификации дифракционных изображений для эксперимента XFEL

1. Методы классификации дифракционных изображений для эксперимента XFEL С.А. Бобков, А.Б. Теслюк, О.Ю. Горобцов, О.М. Ефанов, М.В. Голосова, И.А. Вартанянц, В.А. Ильин Научныйсеминар «Методысуперкомпьютерногомоделирования» 2014

2. Introduction • Free electron lasers (FELs) - new tools to investigate matter at atomic levels • New possibilities for nano-world imaging: • structure • dynamics • processes • Single molecule diffraction • European XFEL – Hamburg • Will become operational at 2016

3. Diffraction before destruction Short Pulse(<50 fs) Long Pulse • Capture an image before the sample has time to respond • This principle is not restricted to tiny samples

4. X-Ray diffraction from single molecule • No crystal, no Bragg peak • Continuous diffraction pattern • The pattern changes as the sample rotates • One pulse, one measurement • Random hits in random orientations

5. XFEL Coherent imaging • Electron energy up to 14.3 GeV • 27 000 FEL pulses per second • Wavelength ~ 6Å • Pulse time ~ 10 fs

6. New experiment – new challenges • IT Infrastructure • 2.3billions of diffraction images daily • Big data needs management: storage, transfer, indexing, publishing • New data – new analysis methods • images are not reproducible • particle orientation is random • molecular dynamics

7. The Task • We present a new method for automated diffraction images sorting • Can be used for: • Uninformative images filtering • To get high quality images for structure reconstruction • To select diffraction images from a particular molecule • Images datasets indexing and search

8. Images feature extraction • A new method for feature extraction is required • Visual descriptors from computer vision methods doesn’t work • Connect spatial structure with diffraction images

9. Feature vector – CCF spectrum Cross correlation function • Autocorrelation, q1 = q2

10. The Method • Calculate feature vectors for diffraction patterns • Use some images as a learning dataset for some machine learning algorithm • Classify the rest

11. The Model Data • Three types of diffraction images Water Adenovirus capsid 2bwt

12. Algorithm Data Matrix Feature vector calculation Principle component analysis

13. Simulated data results All three image classes can be separated from each other

14. What about experimental data?

15. Experimental data Dataset from LCLS (Stanford), two types of molecules First type Second type Empty pattern

16. Algorithm improvements • Particle position estimation for every pattern • Variable bounds

17. Algorithm improvements Support vector machine (SVM) for machine learning • Provide better results than PCA Data Matrix Support vector machine Feature vector calculation

18. Experimental data results • SVM-based method successfully separates two classes of molecules • Empty patterns were classified and filtered out • More than 85 percent of images were separated properly

19. IT Background • We use Python + Numpy + Intel MKL • OpenMP parallelization • 24 Core server – realtime image processing • Kurchatov Supercomputer Centre (complex for modeling and data analysis for mega-facilities)

20. Summary • We have presented a method for diffraction pattern classification • Our method was tested on simulated and experimental data and it works! • The method will be used to develop a software for automatic data clustering, separation, indexing and search • Special particle database will allow quickly analyze experimental data

21. Thank you!